Intercultural Exchange: An Approach to Training from a Franco-Canadian Perspective
Bibliographic record
Abstract
OBJECTIVE: The current challenges of cultural diversity necessitate effective methods for training professionals in health, as well as other sectors, to work with the phenomenon of culture. This paper presents an overview of a semiotic-based approach to training in this regard. METHODS: Recent publications by Anti Randviir in semiotics on the textual nature of cultural phenomena and by Annabel Levesque on the healthcare issues of Western, French-speaking Canadians provide the methodological frame and basic cultural reference for the overview. RESULTS: The anthropological definition of culture as a 'semiotic', or universe of meaning, offers interdisciplinary common ground for designing practical approaches to cultural analysis, intercultural communication and creativity training. This definition is consistent with convergent findings and research practice in social and cognitive psychology, administrative science, philosophy, ethnography, linguistics and semiotics. CONCLUSIONS: Cultural performances such as narrative constitute an effective methodological tool for interdisciplinary data gathering and for analysis of all kinds of cultures: organizational, family, ethnic, regional, transborder, etc. When combined with a functionalist and systemic approach to the study of culture, semiotic approaches to narrative analysis provide useful principles for decoding cultural modes of communication and for designing meaningful change based on cultural specificity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".